library(data.table) #Load all required packages
library(rvest)
library(lubridate)
library(dbplyr)
library(DataComputing)
library(magrittr)
library(Hmisc)
library(party)
library(rpart)
rm(list = ls()) #Clean up environment
Background Information
This dataset contains policy data for 50 US states and DC from 2001 to 2017. Data include information related to state legislation and regulations on nutrition, physical activity, and obesity in settings such as early care and education centers, restaurants, schools, work places, and others. To identify individual bills, use the identifier ProvisionID. A bill or citation may appear more than once because it could apply to multiple health or policy topics, settings, or states.
getwd() #Get working directory
[1] "/Users/fun.k/Final-Project"
setwd("~/Downloads") #Set working directory
The working directory was changed to /Users/fun.k/Downloads inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.
untidy <-read.csv("CDC_Nutrition__Physical_Activity__and_Obesity_-_Legislation.csv") #Read file in table format and create a data frame from it
gc(reset=TRUE) #Change from data frame to data table
used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
Ncells 4762924 254.4 14325560 765.1 NA 4762924 254.4
Vcells 39564123 301.9 737229560 5624.7 16384 39564123 301.9
tracemem(untidy)
[1] "<0x10bd31a30>"
untidy <- as.data.table(untidy)
tracemem[0x10bd31a30 -> 0x1b8ad8f70]: copy as.data.table.data.frame as.data.table
tracemem[0x10bd31a30 -> 0x10bd78890]: as.list.data.frame as.list vapply copy as.data.table.data.frame as.data.table
gc()
used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
Ncells 4762816 254.4 14325560 765.1 NA 4768113 254.7
Vcells 39565992 301.9 589783648 4499.7 16384 40034218 305.5
untidy #Take a look
#Clean Data
#Change variables to start with lowercase/ make easier to use
Legislation <-
untidy %>%
rename(year = Year, quarter = Quarter, state = LocationAbbr, healthTopic = HealthTopic, policyTopic = PolicyTopic, dataSource = DataSource, setting = Setting, title = Title, status = Status, citation = Citation, statusAltValue = StatusAltValue, dataType = DataType, comments = Comments, enDate = EnactedDate, effDate = EffectiveDate, coordinates = GeoLocation, display = DisplayOrder)
Legislation
Which year passed the most legislation?
yearlyData <-
Legislation %>%
group_by(year) %>%
summarise(total = sum(year)) %>%
mutate(total = total/1000)
yearlyData %>%
ggplot(aes(x=year,y=total ))+
geom_bar(stat='identity',position='stack', width=.9, fill = "red") +
ylab("Total (1000s)") +
xlab("Year")

Each bar in the chart represents one year. One can see that in 2011, the most legislation/regulation has been passed between 2001 and 2017.
Legislation vs. Regulation by Topic
Legislation %>%
group_by(year, healthTopic, PolicyTypeID) %>%
summarise(total = sum(year)) %>%
mutate(total = total/1000) %>%
ggplot(aes(x= year, y = total)) +
geom_point(color = "red") +
facet_grid(PolicyTypeID ~ healthTopic) +
ylab("total (1000s)") +
xlab("Year")

As we can see, there has been a lot less regulation than legislation. Nutrititional legislation appears to be the most popular as represented by the many glyphs on the graph. Regulation is less popular and shows little to no type of trend in the graph. In each of the graphs, around 2011 there is peak which corresponds with our finding earlier about 2011 being the most popular year.
Legislation on a map
stateData <-
Legislation %>%
group_by(state, year) %>%
mutate(total = sum(year))
stateData
USMap(data = stateData, key = "state", fill = "total")
Mapping API still under development and may change in future releases.

From this graph, we can see that states like New York, California, Washington, and Illnois, and Hawaii are weaker than most when it comes to passing legislation on nutrition, obesity, and physical activity.
Timeline of Legislation By Setting
Legislation %>%
group_by(year, setting, PolicyTypeID) %>%
summarise(total = sum(year)) %>%
mutate(total = total/1000) %>%
ggplot(aes(x = year, y = total, color = setting)) +
geom_line() +
ylab("total (1000s)") +
facet_wrap(~PolicyTypeID)

In this graph we still see a peak in 2011, but we can also now see what the bills were passed for. Community takes the lead in both regulation and legislation.
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